Overview

Dataset statistics

Number of variables17
Number of observations14585
Missing cells21956
Missing cells (%)8.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory180.2 B

Variable types

Numeric14
Categorical3

Alerts

id is highly overall correlated with country_id and 1 other fieldsHigh correlation
match_api_id is highly overall correlated with seasonHigh correlation
B365H is highly overall correlated with BWH and 8 other fieldsHigh correlation
BWH is highly overall correlated with B365H and 8 other fieldsHigh correlation
IWH is highly overall correlated with B365H and 8 other fieldsHigh correlation
LBH is highly overall correlated with B365H and 8 other fieldsHigh correlation
PSH is highly overall correlated with B365H and 8 other fieldsHigh correlation
WHH is highly overall correlated with B365H and 8 other fieldsHigh correlation
SJH is highly overall correlated with B365H and 8 other fieldsHigh correlation
VCH is highly overall correlated with B365H and 8 other fieldsHigh correlation
GBH is highly overall correlated with B365H and 8 other fieldsHigh correlation
BSH is highly overall correlated with B365H and 8 other fieldsHigh correlation
country_id is highly overall correlated with id and 1 other fieldsHigh correlation
league_id is highly overall correlated with id and 1 other fieldsHigh correlation
season is highly overall correlated with match_api_idHigh correlation
PSH has 7293 (50.0%) missing valuesMissing
SJH has 3511 (24.1%) missing valuesMissing
GBH has 5504 (37.7%) missing valuesMissing
BSH has 5500 (37.7%) missing valuesMissing
home_team_api_id is highly skewed (γ1 = 21.86459231)Skewed
away_team_api_id is highly skewed (γ1 = 21.86459323)Skewed
season is uniformly distributedUniform
id has unique valuesUnique
match_api_id has unique valuesUnique

Reproduction

Analysis started2023-02-28 18:16:47.684155
Analysis finished2023-02-28 18:17:10.983415
Duration23.3 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct14585
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10739.324
Minimum1729
Maximum24557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:11.058572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1729
5-th percentile2458.2
Q15375
median9021
Q312667
95-th percentile23827.8
Maximum24557
Range22828
Interquartile range (IQR)7292

Descriptive statistics

Standard deviation6984.3738
Coefficient of variation (CV)0.65035505
Kurtosis-0.59158609
Mean10739.324
Median Absolute Deviation (MAD)3646
Skewness0.82586204
Sum1.5663304 × 108
Variance48781478
MonotonicityStrictly increasing
2023-02-28T13:17:11.177599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1729 1
 
< 0.1%
11471 1
 
< 0.1%
11445 1
 
< 0.1%
11446 1
 
< 0.1%
11447 1
 
< 0.1%
11448 1
 
< 0.1%
11449 1
 
< 0.1%
11450 1
 
< 0.1%
11451 1
 
< 0.1%
11452 1
 
< 0.1%
Other values (14575) 14575
99.9%
ValueCountFrequency (%)
1729 1
< 0.1%
1730 1
< 0.1%
1731 1
< 0.1%
1732 1
< 0.1%
1733 1
< 0.1%
1734 1
< 0.1%
1735 1
< 0.1%
1736 1
< 0.1%
1737 1
< 0.1%
1738 1
< 0.1%
ValueCountFrequency (%)
24557 1
< 0.1%
24556 1
< 0.1%
24555 1
< 0.1%
24554 1
< 0.1%
24553 1
< 0.1%
24552 1
< 0.1%
24551 1
< 0.1%
24550 1
< 0.1%
24549 1
< 0.1%
24548 1
< 0.1%

country_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size227.9 KiB
1729
3040 
4769
3040 
21518
3040 
10257
3017 
7809
2448 

Length

Max length5
Median length4
Mean length4.4152897
Min length4

Characters and Unicode

Total characters64397
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1729
2nd row1729
3rd row1729
4th row1729
5th row1729

Common Values

ValueCountFrequency (%)
1729 3040
20.8%
4769 3040
20.8%
21518 3040
20.8%
10257 3017
20.7%
7809 2448
16.8%

Length

2023-02-28T13:17:11.280622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:17:11.380644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1729 3040
20.8%
4769 3040
20.8%
21518 3040
20.8%
10257 3017
20.7%
7809 2448
16.8%

Most occurring characters

ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64397
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 64397
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

league_id
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size227.9 KiB
1729
3040 
4769
3040 
21518
3040 
10257
3017 
7809
2448 

Length

Max length5
Median length4
Mean length4.4152897
Min length4

Characters and Unicode

Total characters64397
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1729
2nd row1729
3rd row1729
4th row1729
5th row1729

Common Values

ValueCountFrequency (%)
1729 3040
20.8%
4769 3040
20.8%
21518 3040
20.8%
10257 3017
20.7%
7809 2448
16.8%

Length

2023-02-28T13:17:11.478667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:17:11.579690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1729 3040
20.8%
4769 3040
20.8%
21518 3040
20.8%
10257 3017
20.7%
7809 2448
16.8%

Most occurring characters

ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64397
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 64397
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12137
18.8%
7 11545
17.9%
2 9097
14.1%
9 8528
13.2%
5 6057
9.4%
8 5488
8.5%
0 5465
8.5%
4 3040
 
4.7%
6 3040
 
4.7%

season
Categorical

HIGH CORRELATION  UNIFORM 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size227.9 KiB
2008/2009
1826 
2009/2010
1826 
2010/2011
1826 
2012/2013
1826 
2013/2014
1826 
Other values (3)
5455 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters131265
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008/2009
2nd row2008/2009
3rd row2008/2009
4th row2008/2009
5th row2008/2009

Common Values

ValueCountFrequency (%)
2008/2009 1826
12.5%
2009/2010 1826
12.5%
2010/2011 1826
12.5%
2012/2013 1826
12.5%
2013/2014 1826
12.5%
2015/2016 1826
12.5%
2014/2015 1825
12.5%
2011/2012 1804
12.4%

Length

2023-02-28T13:17:11.676712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-28T13:17:11.787737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2008/2009 1826
12.5%
2009/2010 1826
12.5%
2010/2011 1826
12.5%
2012/2013 1826
12.5%
2013/2014 1826
12.5%
2015/2016 1826
12.5%
2014/2015 1825
12.5%
2011/2012 1804
12.4%

Most occurring characters

ValueCountFrequency (%)
0 38300
29.2%
2 32800
25.0%
1 27322
20.8%
/ 14585
 
11.1%
9 3652
 
2.8%
3 3652
 
2.8%
4 3651
 
2.8%
5 3651
 
2.8%
8 1826
 
1.4%
6 1826
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116680
88.9%
Other Punctuation 14585
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38300
32.8%
2 32800
28.1%
1 27322
23.4%
9 3652
 
3.1%
3 3652
 
3.1%
4 3651
 
3.1%
5 3651
 
3.1%
8 1826
 
1.6%
6 1826
 
1.6%
Other Punctuation
ValueCountFrequency (%)
/ 14585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 131265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38300
29.2%
2 32800
25.0%
1 27322
20.8%
/ 14585
 
11.1%
9 3652
 
2.8%
3 3652
 
2.8%
4 3651
 
2.8%
5 3651
 
2.8%
8 1826
 
1.4%
6 1826
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38300
29.2%
2 32800
25.0%
1 27322
20.8%
/ 14585
 
11.1%
9 3652
 
2.8%
3 3652
 
2.8%
4 3651
 
2.8%
5 3651
 
2.8%
8 1826
 
1.4%
6 1826
 
1.4%

match_api_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct14585
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1198544.3
Minimum483129
Maximum2118418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:11.918766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum483129
5-th percentile489392.2
Q1705602
median1216821
Q31709701
95-th percentile2030112.8
Maximum2118418
Range1635289
Interquartile range (IQR)1004099

Descriptive statistics

Standard deviation494169.8
Coefficient of variation (CV)0.41230833
Kurtosis-1.1838785
Mean1198544.3
Median Absolute Deviation (MAD)492886
Skewness0.23033563
Sum1.7480769 × 1010
Variance2.4420379 × 1011
MonotonicityNot monotonic
2023-02-28T13:17:12.041794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489042 1
 
< 0.1%
1083263 1
 
< 0.1%
1083149 1
 
< 0.1%
1083150 1
 
< 0.1%
1083154 1
 
< 0.1%
1083158 1
 
< 0.1%
1083160 1
 
< 0.1%
1083162 1
 
< 0.1%
1083166 1
 
< 0.1%
1083171 1
 
< 0.1%
Other values (14575) 14575
99.9%
ValueCountFrequency (%)
483129 1
< 0.1%
483130 1
< 0.1%
483131 1
< 0.1%
483132 1
< 0.1%
483133 1
< 0.1%
483134 1
< 0.1%
483135 1
< 0.1%
483136 1
< 0.1%
483137 1
< 0.1%
483138 1
< 0.1%
ValueCountFrequency (%)
2118418 1
< 0.1%
2060645 1
< 0.1%
2060644 1
< 0.1%
2060643 1
< 0.1%
2060642 1
< 0.1%
2060641 1
< 0.1%
2060640 1
< 0.1%
2060639 1
< 0.1%
2060638 1
< 0.1%
2060637 1
< 0.1%

home_team_api_id
Real number (ℝ)

Distinct164
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9513.4717
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:12.400875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8178
Q18535
median8686
Q39869
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1334

Descriptive statistics

Standard deviation8097.7772
Coefficient of variation (CV)0.85119055
Kurtosis505.90825
Mean9513.4717
Median Absolute Deviation (MAD)509
Skewness21.864592
Sum1.3875398 × 108
Variance65573996
MonotonicityNot monotonic
2023-02-28T13:17:12.517901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10260 152
 
1.0%
9827 152
 
1.0%
9851 152
 
1.0%
8639 152
 
1.0%
8689 152
 
1.0%
8592 152
 
1.0%
9831 152
 
1.0%
9853 152
 
1.0%
9941 152
 
1.0%
9847 152
 
1.0%
Other values (154) 13065
89.6%
ValueCountFrequency (%)
4087 76
0.5%
4170 19
 
0.1%
6269 18
 
0.1%
6391 19
 
0.1%
7794 76
0.5%
7819 95
0.7%
7869 19
 
0.1%
7878 95
0.7%
7943 57
0.4%
8121 19
 
0.1%
ValueCountFrequency (%)
208931 19
 
0.1%
108893 19
 
0.1%
10281 76
0.5%
10278 19
 
0.1%
10269 136
0.9%
10268 38
 
0.3%
10267 152
1.0%
10261 133
0.9%
10260 152
1.0%
10252 152
1.0%

away_team_api_id
Real number (ℝ)

Distinct164
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9513.6747
Minimum4087
Maximum208931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:12.639929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4087
5-th percentile8178
Q18535
median8686
Q39869
95-th percentile10260
Maximum208931
Range204844
Interquartile range (IQR)1334

Descriptive statistics

Standard deviation8097.7678
Coefficient of variation (CV)0.8511714
Kurtosis505.9084
Mean9513.6747
Median Absolute Deviation (MAD)509
Skewness21.864593
Sum1.3875695 × 108
Variance65573844
MonotonicityNot monotonic
2023-02-28T13:17:12.759955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9748 152
 
1.0%
9831 152
 
1.0%
9941 152
 
1.0%
8636 152
 
1.0%
9847 152
 
1.0%
8639 152
 
1.0%
8592 152
 
1.0%
8686 152
 
1.0%
9827 152
 
1.0%
8564 152
 
1.0%
Other values (154) 13065
89.6%
ValueCountFrequency (%)
4087 76
0.5%
4170 19
 
0.1%
6269 18
 
0.1%
6391 19
 
0.1%
7794 76
0.5%
7819 95
0.7%
7869 19
 
0.1%
7878 95
0.7%
7943 57
0.4%
8121 19
 
0.1%
ValueCountFrequency (%)
208931 19
 
0.1%
108893 19
 
0.1%
10281 76
0.5%
10278 19
 
0.1%
10269 136
0.9%
10268 38
 
0.3%
10267 152
1.0%
10261 133
0.9%
10260 152
1.0%
10252 152
1.0%

B365H
Real number (ℝ)

Distinct118
Distinct (%)0.8%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.5956063
Minimum1.04
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:12.881982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.75
95-th percentile6
Maximum26
Range24.96
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation1.7569991
Coefficient of variation (CV)0.67691278
Kurtosis23.559478
Mean2.5956063
Median Absolute Deviation (MAD)0.5
Skewness3.9382983
Sum37825.77
Variance3.0870457
MonotonicityNot monotonic
2023-02-28T13:17:13.005011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 696
 
4.8%
2 630
 
4.3%
2.5 506
 
3.5%
2.2 504
 
3.5%
1.91 498
 
3.4%
2.4 401
 
2.7%
1.44 396
 
2.7%
2.3 383
 
2.6%
2.25 382
 
2.6%
1.8 328
 
2.2%
Other values (108) 9849
67.5%
ValueCountFrequency (%)
1.04 5
 
< 0.1%
1.05 17
 
0.1%
1.06 21
 
0.1%
1.07 12
 
0.1%
1.08 28
0.2%
1.09 9
 
0.1%
1.1 34
0.2%
1.11 14
 
0.1%
1.13 50
0.3%
1.14 56
0.4%
ValueCountFrequency (%)
26 2
 
< 0.1%
23 1
 
< 0.1%
21 3
 
< 0.1%
19 5
 
< 0.1%
17 7
 
< 0.1%
15 24
0.2%
14 1
 
< 0.1%
13 41
0.3%
12 26
0.2%
11 23
0.2%

BWH
Real number (ℝ)

Distinct220
Distinct (%)1.5%
Missing27
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.541709
Minimum1.03
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:13.128039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile1.25
Q11.7
median2.1
Q32.7
95-th percentile5.5
Maximum34
Range32.97
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6254011
Coefficient of variation (CV)0.63949141
Kurtosis27.356785
Mean2.541709
Median Absolute Deviation (MAD)0.5
Skewness3.9208926
Sum37002.2
Variance2.6419287
MonotonicityNot monotonic
2023-02-28T13:17:13.246065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 479
 
3.3%
2.1 441
 
3.0%
2.05 434
 
3.0%
1.95 426
 
2.9%
2.15 396
 
2.7%
2.25 373
 
2.6%
2.3 357
 
2.4%
2.2 355
 
2.4%
2.4 349
 
2.4%
1.75 336
 
2.3%
Other values (210) 10612
72.8%
ValueCountFrequency (%)
1.03 2
 
< 0.1%
1.04 1
 
< 0.1%
1.05 13
 
0.1%
1.06 15
 
0.1%
1.07 12
 
0.1%
1.08 33
0.2%
1.09 15
 
0.1%
1.1 28
0.2%
1.11 21
0.1%
1.12 40
0.3%
ValueCountFrequency (%)
34 1
 
< 0.1%
21 3
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 2
< 0.1%
16.5 2
< 0.1%
16 3
< 0.1%
15.5 2
< 0.1%
15 3
< 0.1%
14.5 2
< 0.1%

IWH
Real number (ℝ)

Distinct146
Distinct (%)1.0%
Missing45
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2.4544746
Minimum1.05
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:13.372093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.25
Q11.7
median2.1
Q32.6
95-th percentile5.2
Maximum20
Range18.95
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.4342161
Coefficient of variation (CV)0.58432715
Kurtosis18.017313
Mean2.4544746
Median Absolute Deviation (MAD)0.45
Skewness3.5009461
Sum35688.06
Variance2.0569759
MonotonicityNot monotonic
2023-02-28T13:17:13.492121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 903
 
6.2%
2.1 849
 
5.8%
2.2 794
 
5.4%
1.9 691
 
4.7%
2.3 683
 
4.7%
2.4 556
 
3.8%
1.8 513
 
3.5%
2.5 470
 
3.2%
2.6 463
 
3.2%
1.85 392
 
2.7%
Other values (136) 8226
56.4%
ValueCountFrequency (%)
1.05 14
 
0.1%
1.07 33
 
0.2%
1.08 8
 
0.1%
1.1 46
0.3%
1.11 7
 
< 0.1%
1.12 60
0.4%
1.13 2
 
< 0.1%
1.15 85
0.6%
1.17 64
0.4%
1.18 7
 
< 0.1%
ValueCountFrequency (%)
20 2
 
< 0.1%
15 4
 
< 0.1%
14 9
0.1%
13 9
0.1%
12.5 5
 
< 0.1%
12 17
0.1%
11.5 2
 
< 0.1%
11 13
0.1%
10.5 5
 
< 0.1%
10.3 9
0.1%

LBH
Real number (ℝ)

Distinct119
Distinct (%)0.8%
Missing17
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.5125048
Minimum1.04
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:13.620148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.7
95-th percentile5.5
Maximum26
Range24.96
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation1.6065461
Coefficient of variation (CV)0.63942012
Kurtosis23.038864
Mean2.5125048
Median Absolute Deviation (MAD)0.48
Skewness3.8605401
Sum36602.17
Variance2.5809905
MonotonicityNot monotonic
2023-02-28T13:17:13.745178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 687
 
4.7%
2 653
 
4.5%
2.2 547
 
3.8%
2.25 498
 
3.4%
1.8 465
 
3.2%
2.5 461
 
3.2%
1.91 435
 
3.0%
2.4 356
 
2.4%
2.38 337
 
2.3%
1.83 332
 
2.3%
Other values (109) 9797
67.2%
ValueCountFrequency (%)
1.04 3
 
< 0.1%
1.05 12
 
0.1%
1.06 6
 
< 0.1%
1.07 21
0.1%
1.08 25
0.2%
1.09 19
0.1%
1.1 33
0.2%
1.11 19
0.1%
1.12 36
0.2%
1.13 5
 
< 0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
19 3
 
< 0.1%
17 5
 
< 0.1%
15 14
0.1%
13 27
0.2%
12 28
0.2%
11 23
0.2%
10.5 1
 
< 0.1%

PSH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct795
Distinct (%)10.9%
Missing7293
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean2.7874011
Minimum1.04
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:13.865205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.26
Q11.71
median2.18
Q32.94
95-th percentile6.55
Maximum36
Range34.96
Interquartile range (IQR)1.23

Descriptive statistics

Standard deviation2.1918452
Coefficient of variation (CV)0.78634009
Kurtosis31.226705
Mean2.7874011
Median Absolute Deviation (MAD)0.55
Skewness4.5013018
Sum20325.729
Variance4.8041855
MonotonicityNot monotonic
2023-02-28T13:17:13.982231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.93 67
 
0.5%
1.83 57
 
0.4%
1.85 56
 
0.4%
2.08 56
 
0.4%
1.88 55
 
0.4%
1.65 54
 
0.4%
1.47 54
 
0.4%
2.1 53
 
0.4%
2.01 50
 
0.3%
2.07 50
 
0.3%
Other values (785) 6740
46.2%
(Missing) 7293
50.0%
ValueCountFrequency (%)
1.04 1
 
< 0.1%
1.05 1
 
< 0.1%
1.06 9
0.1%
1.07 11
0.1%
1.08 10
0.1%
1.09 13
0.1%
1.1 19
0.1%
1.11 15
0.1%
1.12 9
0.1%
1.13 16
0.1%
ValueCountFrequency (%)
36 1
< 0.1%
31.13 1
< 0.1%
25.51 1
< 0.1%
23.6 1
< 0.1%
23 1
< 0.1%
22.4 1
< 0.1%
22.3 1
< 0.1%
21.2 1
< 0.1%
21.1 1
< 0.1%
21.08 1
< 0.1%

WHH
Real number (ℝ)

Distinct116
Distinct (%)0.8%
Missing17
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.5692854
Minimum1.02
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:14.106258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile1.29
Q11.7
median2.15
Q32.7
95-th percentile5.5
Maximum26
Range24.98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6885185
Coefficient of variation (CV)0.65719383
Kurtosis23.850584
Mean2.5692854
Median Absolute Deviation (MAD)0.48
Skewness3.9699912
Sum37429.35
Variance2.8510948
MonotonicityNot monotonic
2023-02-28T13:17:14.227286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 536
 
3.7%
2 515
 
3.5%
2.1 504
 
3.5%
2.3 497
 
3.4%
2.4 485
 
3.3%
2.2 444
 
3.0%
1.91 430
 
2.9%
2.05 395
 
2.7%
2.25 381
 
2.6%
1.44 370
 
2.5%
Other values (106) 10011
68.6%
ValueCountFrequency (%)
1.02 1
 
< 0.1%
1.04 2
 
< 0.1%
1.05 9
 
0.1%
1.06 14
 
0.1%
1.07 7
 
< 0.1%
1.08 36
0.2%
1.1 45
0.3%
1.11 22
 
0.2%
1.12 26
 
0.2%
1.14 72
0.5%
ValueCountFrequency (%)
26 1
 
< 0.1%
23 1
 
< 0.1%
21 4
 
< 0.1%
19 2
 
< 0.1%
17 8
 
0.1%
15 25
0.2%
13 19
0.1%
12 38
0.3%
11 33
0.2%
10.5 2
 
< 0.1%

SJH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct124
Distinct (%)1.1%
Missing3511
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean2.5335371
Minimum1.04
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:14.349313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.25
Q11.67
median2.1
Q32.7
95-th percentile5.5
Maximum23
Range21.96
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation1.6279278
Coefficient of variation (CV)0.64255141
Kurtosis19.63822
Mean2.5335371
Median Absolute Deviation (MAD)0.48
Skewness3.6768224
Sum28056.39
Variance2.6501491
MonotonicityNot monotonic
2023-02-28T13:17:14.463339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 589
 
4.0%
2 491
 
3.4%
2.2 434
 
3.0%
2.5 394
 
2.7%
1.83 313
 
2.1%
2.25 308
 
2.1%
1.73 306
 
2.1%
2.38 301
 
2.1%
2.3 297
 
2.0%
1.44 295
 
2.0%
Other values (114) 7346
50.4%
(Missing) 3511
24.1%
ValueCountFrequency (%)
1.04 1
 
< 0.1%
1.05 10
0.1%
1.06 10
0.1%
1.07 10
0.1%
1.08 18
0.1%
1.09 7
 
< 0.1%
1.1 18
0.1%
1.11 19
0.1%
1.12 3
 
< 0.1%
1.13 24
0.2%
ValueCountFrequency (%)
23 1
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
17 3
 
< 0.1%
15 14
0.1%
14 1
 
< 0.1%
13 24
0.2%
12 10
 
0.1%
11 34
0.2%
10.5 1
 
< 0.1%

VCH
Real number (ℝ)

Distinct156
Distinct (%)1.1%
Missing30
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.6429395
Minimum1.03
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:14.582366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile1.25
Q11.7
median2.15
Q32.8
95-th percentile6
Maximum36
Range34.97
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.916398
Coefficient of variation (CV)0.72510095
Kurtosis36.030799
Mean2.6429395
Median Absolute Deviation (MAD)0.5
Skewness4.6880719
Sum38467.985
Variance3.6725812
MonotonicityNot monotonic
2023-02-28T13:17:14.699392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 547
 
3.8%
2 513
 
3.5%
2.2 474
 
3.2%
2.3 405
 
2.8%
2.5 404
 
2.8%
2.05 394
 
2.7%
2.25 379
 
2.6%
2.4 308
 
2.1%
2.15 308
 
2.1%
1.8 302
 
2.1%
Other values (146) 10521
72.1%
ValueCountFrequency (%)
1.03 2
 
< 0.1%
1.04 5
 
< 0.1%
1.05 6
 
< 0.1%
1.06 29
0.2%
1.07 13
0.1%
1.08 9
 
0.1%
1.083 1
 
< 0.1%
1.09 23
0.2%
1.1 26
0.2%
1.11 18
0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
31 1
 
< 0.1%
29 2
 
< 0.1%
26 1
 
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
21 4
< 0.1%
20 3
< 0.1%
19 7
< 0.1%
18 7
< 0.1%

GBH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct150
Distinct (%)1.7%
Missing5504
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean2.4724259
Minimum1.05
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:14.829420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.27
Q11.7
median2.1
Q32.63
95-th percentile5
Maximum21
Range19.95
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation1.4547078
Coefficient of variation (CV)0.58837264
Kurtosis19.317445
Mean2.4724259
Median Absolute Deviation (MAD)0.45
Skewness3.5702975
Sum22452.1
Variance2.1161747
MonotonicityNot monotonic
2023-02-28T13:17:14.947447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 425
 
2.9%
2 410
 
2.8%
2.2 278
 
1.9%
2.4 266
 
1.8%
2.3 261
 
1.8%
2.5 253
 
1.7%
1.95 235
 
1.6%
1.9 228
 
1.6%
2.25 225
 
1.5%
1.8 217
 
1.5%
Other values (140) 6283
43.1%
(Missing) 5504
37.7%
ValueCountFrequency (%)
1.05 3
 
< 0.1%
1.06 10
0.1%
1.07 6
 
< 0.1%
1.08 6
 
< 0.1%
1.09 5
 
< 0.1%
1.1 17
0.1%
1.11 1
 
< 0.1%
1.12 18
0.1%
1.13 9
0.1%
1.14 9
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
17 3
 
< 0.1%
16 1
 
< 0.1%
15 3
 
< 0.1%
14 1
 
< 0.1%
13 8
0.1%
12 5
 
< 0.1%
11.5 3
 
< 0.1%
11 16
0.1%
10.5 5
 
< 0.1%

BSH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)1.1%
Missing5500
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean2.4657259
Minimum1.04
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size227.9 KiB
2023-02-28T13:17:15.071475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.29
Q11.67
median2.1
Q32.6
95-th percentile5
Maximum17
Range15.96
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation1.4605436
Coefficient of variation (CV)0.59233818
Kurtosis18.447148
Mean2.4657259
Median Absolute Deviation (MAD)0.43
Skewness3.5678767
Sum22401.12
Variance2.1331876
MonotonicityNot monotonic
2023-02-28T13:17:15.186501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 414
 
2.8%
2 383
 
2.6%
2.5 340
 
2.3%
1.91 317
 
2.2%
2.3 304
 
2.1%
1.83 299
 
2.1%
2.4 296
 
2.0%
2.2 281
 
1.9%
2.25 277
 
1.9%
1.44 252
 
1.7%
Other values (86) 5922
40.6%
(Missing) 5500
37.7%
ValueCountFrequency (%)
1.04 1
 
< 0.1%
1.05 1
 
< 0.1%
1.06 7
 
< 0.1%
1.07 11
 
0.1%
1.08 10
 
0.1%
1.1 12
 
0.1%
1.11 11
 
0.1%
1.12 22
 
0.2%
1.14 40
0.3%
1.17 59
0.4%
ValueCountFrequency (%)
17 5
 
< 0.1%
15 4
 
< 0.1%
13 7
 
< 0.1%
12 12
0.1%
11 21
0.1%
10 22
0.2%
9.5 3
 
< 0.1%
9 27
0.2%
8.5 26
0.2%
8 22
0.2%

Interactions

2023-02-28T13:17:08.867458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:48.549600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.040832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.606202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.318576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.785922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.236175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.982769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.494121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.966398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.690818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.168485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.560870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.057220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.989484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:48.650622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.151857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.704213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.416598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.881954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.336202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.084800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.590154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.066421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.789841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.262506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.666895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.155242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.116513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:48.776651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.276892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.817238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.534624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.993878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.453260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.203820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.703168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.170444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.906247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.365541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.783921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.274268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.221549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:48.875673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.390917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.924262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.634647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.095908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.556298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.307854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.804137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.273478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.005212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.464617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.885951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.604172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.324574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:48.973703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.496941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.023285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.733680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.195930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.660321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.413877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.908160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.373512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.103235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.563639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.988976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.710196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.424595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.077613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.606966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.132309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.836703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.296953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.771497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.519902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.007183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.474524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.202257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.656661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.092004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.835224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.533628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.184639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.721992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.241334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.941727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.408979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.885533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.633927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.118207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.580558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.316293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.760686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.201038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:07.963253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.638651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.298665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.838018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.357359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.049751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.518003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.004550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.752954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.226231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.689583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.432319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.860713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.318053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.071279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.742674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.401689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:50.944042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.457382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.156765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.619025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.106583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.855978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.324254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.794606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.539344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.963736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.418075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.192304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.840697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.509712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.050066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.791457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.262788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.727050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.218597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:58.970003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.433278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:01.915633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.650379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.062758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.526099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.301329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:09.939719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.614736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.159090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.892480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.360810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.828073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.323632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.073026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.540303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.021656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.762394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.158780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.628123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.403352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:10.038740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.712759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.261113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:52.998504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.461833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:55.924094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.425643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.169059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.642325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.143684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.857415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.250801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.725145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.508375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:10.146765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.816781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.376138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.101527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.566873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.027117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.757718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.279072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.745349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.251709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:03.961438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.348823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.835180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.620401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:10.255791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:49.922806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:51.491165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:53.209552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:54.676897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:56.129140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:57.870744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:16:59.387097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:00.854374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:02.366734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:04.065462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:05.452847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:06.944193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-28T13:17:08.734426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-28T13:17:15.302527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
idmatch_api_idhome_team_api_idaway_team_api_idB365HBWHIWHLBHPSHWHHSJHVCHGBHBSHcountry_idleague_idseason
id1.0000.293-0.085-0.084-0.031-0.018-0.019-0.021-0.024-0.023-0.026-0.021-0.025-0.0250.8600.8600.252
match_api_id0.2931.000-0.026-0.0260.0240.0480.0390.0570.0220.0620.0010.058-0.0060.0080.0000.0001.000
home_team_api_id-0.085-0.0261.000-0.029-0.052-0.054-0.057-0.057-0.049-0.054-0.057-0.053-0.063-0.0640.0690.0690.093
away_team_api_id-0.084-0.026-0.0291.0000.0550.0560.0600.0560.0550.0540.0600.0530.0610.0610.0690.0690.093
B365H-0.0310.024-0.0520.0551.0000.9950.9900.9930.9980.9950.9950.9960.9970.9950.0730.0730.027
BWH-0.0180.048-0.0540.0560.9951.0000.9910.9930.9970.9940.9930.9950.9960.9940.0760.0760.020
IWH-0.0190.039-0.0570.0600.9900.9911.0000.9900.9890.9880.9890.9880.9900.9900.0840.0840.034
LBH-0.0210.057-0.0570.0560.9930.9930.9901.0000.9950.9940.9910.9940.9920.9920.0770.0770.032
PSH-0.0240.022-0.0490.0550.9980.9970.9890.9951.0000.9970.9970.9990.9960.9950.0750.0750.029
WHH-0.0230.062-0.0540.0540.9950.9940.9880.9940.9971.0000.9920.9960.9940.9930.0780.0780.034
SJH-0.0260.001-0.0570.0600.9950.9930.9890.9910.9970.9921.0000.9920.9940.9930.0900.0900.023
VCH-0.0210.058-0.0530.0530.9960.9950.9880.9940.9990.9960.9921.0000.9940.9940.0750.0750.030
GBH-0.025-0.006-0.0630.0610.9970.9960.9900.9920.9960.9940.9940.9941.0000.9950.0850.0850.024
BSH-0.0250.008-0.0640.0610.9950.9940.9900.9920.9950.9930.9930.9940.9951.0000.0870.0870.014
country_id0.8600.0000.0690.0690.0730.0760.0840.0770.0750.0780.0900.0750.0850.0871.0001.0000.000
league_id0.8600.0000.0690.0690.0730.0760.0840.0770.0750.0780.0900.0750.0850.0871.0001.0000.000
season0.2521.0000.0930.0930.0270.0200.0340.0320.0290.0340.0230.0300.0240.0140.0000.0001.000

Missing values

2023-02-28T13:17:10.428829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-28T13:17:10.652879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-28T13:17:10.858387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idcountry_idleague_idseasonmatch_api_idhome_team_api_idaway_team_api_idB365HBWHIWHLBHPSHWHHSJHVCHGBHBSH
17281729172917292008/200948904210260102611.291.301.301.25NaN1.251.251.281.301.29
17291730172917292008/2009489043982586591.201.221.201.20NaN1.171.201.251.221.22
17301731172917292008/2009489044847286505.505.004.504.50NaN5.504.335.505.004.50
17311732172917292008/2009489045865485281.911.901.801.80NaN1.831.911.901.911.91
17321733172917292008/20094890461025284561.911.952.001.83NaN1.911.911.901.911.91
17331734172917292008/2009489047866886552.001.852.001.80NaN1.952.002.052.002.00
17341735172917292008/2009489048854985863.202.802.902.80NaN2.902.883.203.002.80
17351736172917292008/20094890498559101941.831.751.751.73NaN1.801.801.851.831.80
17361737172917292008/2009489050866798792.602.452.402.40NaN2.502.382.602.602.60
17371738172917292008/2009489051845584621.331.301.301.29NaN1.301.251.331.331.33
idcountry_idleague_idseasonmatch_api_idhome_team_api_idaway_team_api_idB365HBWHIWHLBHPSHWHHSJHVCHGBHBSH
245472454821518215182015/20162030162863483721.111.111.101.111.131.14NaN1.11NaNNaN
245482454921518215182015/20162030163830283051.441.481.501.441.501.50NaN1.50NaNNaN
245492455021518215182015/201620301648306102053.503.253.303.503.723.50NaN3.60NaNNaN
245502455121518215182015/20162030165991086333.803.903.803.604.023.75NaN3.90NaNNaN
245512455221518215182015/20162030166858185602.632.702.602.622.782.70NaN2.75NaNNaN
245522455321518215182015/201620301679906102671.571.571.651.571.581.62NaN1.57NaNNaN
245532455421518215182015/20162030168986497832.252.352.202.252.362.38NaN2.30NaNNaN
245542455521518215182015/20162030169831598691.531.551.601.501.551.57NaN1.55NaNNaN
245552455621518215182015/20162030170787886032.302.352.402.302.342.40NaN2.30NaNNaN
245562455721518215182015/20162030171837085582.202.252.302.102.202.20NaN2.20NaNNaN